Fleig-Goldstein, Brendan (2026) A Topological Learning-Theoretic Justification for Bounded Rational Analysis as a Methodological Strategy in Cognitive Science. In: UNSPECIFIED.

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Abstract

This paper presents a topological learning-theoretic analysis of an approach in cognitive science called bounded rational analysis. In this approach, modelers begin by deriving an optimal cognitive model, then use discrepancies between idealized calculations and observed human behavior to identify psychological constraints (e.g., memory limits), and incorporate such constraints into a newly derived resource-optimal model in an iterative scientific process. I show that this de-idealization process exploits an epistemic asymmetry: models positing greater rationality are more falsifiable. A methodological preference for more rational models can therefore be epistemically justified via learning theory.

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Item Type:	Conference or Workshop Item (UNSPECIFIED)
Creators:	Creators Email ORCID Fleig-Goldstein, Brendan brendan_fleig-goldstein@brown.edu 0009-0005-5462-522X
Keywords:	bounded rationality, bounded rational analysis, resource rationality, cognitive science, learning theory, formal epistemology, scientific method, evidence
Subjects:	Specific Sciences > Cognitive Science General Issues > Evidence General Issues > Formal Learning Theory
Depositing User:	Dr. Brendan Fleig-Goldstein
Date Deposited:	11 Jun 2026 12:31
Last Modified:	11 Jun 2026 12:31
Item ID:	30045
Subjects:	Specific Sciences > Cognitive Science General Issues > Evidence General Issues > Formal Learning Theory
Date:	28 May 2026
URI:	https://philsci-archive.pitt.edu/id/eprint/30045

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• A Topological Learning-Theoretic Justification for Bounded Rational Analysis as a Methodological Strategy in Cognitive Science. (deposited 04 Jun 2026 14:04)

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